Search results for "User profile"

showing 10 items of 26 documents

OLAP Personalization with User-Describing Profiles

2010

In this paper we have highlighted five existing approaches for introducing personalization in OLAP: preference constructors, dynamic personalization, visual OLAP, recommendations with user session analysis and recommendations with user profile analysis and have analyzed research papers within these directions. We have pointed out applicability of personalization to OLAP schema elements in these approaches. The comparative analysis has been made in order to highlight a certain personalization approach. A new method has been proposed, which provides exhaustive description of interaction between user and data warehouse, using the concept of Zachman Framework [1, 2], according to which a set of…

User profileData collectionInformation retrievalComputer scienceOnline analytical processingSchema (psychology)Zachman FrameworkData miningcomputer.software_genrecomputerData warehousePersonalization
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Extracting Features from Social Media Networks Using Semantics

2016

This paper focuses on the analysis of social media content generated by social networks (e.g. Twitter) in order to extract semantic features. By using text categorization to sort text feeds into categories of similar feeds, it has been proved to reduce the overhead that is required to retrieve these feeds and at the same time, it provides smaller pools in which further investigations can be made easier. The aim of this survey is to draw a user profile, by analysing his or her tweets. In this early stage of research, being a pre-processing phase, a dictionary based approach is considered. Moreover, the paper describes an algorithm used in analysing the text and its preliminary results. This …

User profileInformation retrievalComputer sciencebusiness.industrySemantic analysis (machine learning)Feature extractioncomputer.software_genreSemanticsSupport vector machinesortOverhead (computing)Social mediaArtificial intelligencebusinesscomputerNatural language processing
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WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach

2020

The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture - called WhoSNext (WSN) - tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a …

User profileInformation retrievalSocial networkbusiness.industryComputer sciencesocial networkingmedia_common.quotation_subjectTwitterKnowledge engineeringspreading activation network020207 software engineering02 engineering and technologyRecommender systemFriendshipContent analysis0202 electrical engineering electronic engineering information engineeringGraph (abstract data type)020201 artificial intelligence & image processingData pre-processingRecommender systembusinessWireless sensor networksocial users recommendationmedia_common2020 International Conference on Data Mining Workshops (ICDMW)
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A data-logging mechanism to support e-learning systems

2015

User profiling and e-learning have received great attention in the last years. In a learning environment, user profiling provides historical data of the students' performance on different learning subjects. e-Learning tools such as serious games can collect user's data and build a user profile by appropriate storing these information. Thus, by combining above techniques and analysing each user's data a teacher can provide personalized treatment to her/his students. In this paper we present our solution on storing and analysing user data on a centralized server. Our system stores data collected from users with dyslexia. We maintain one user profile per student from data taken from a) user re…

User profileMultimediaComputer scienceUser modelingLearning environmentDyslexiaComputer user satisfactionmedicine.diseasecomputer.software_genreUser interface designData loggermedicineProfiling (information science)computer2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)
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An approach for recommending personalized contents for homecare users in the context of health 2.0

2014

This paper proposes a content recommendation mechanism as part of a model for implementing ubiquitous learning for supporting people with chronic diseases who are treated at home, so that they can learn more about treatments for their disease. In the proposed approach, the learning takes place based on day-to-day activities and real situations. In this case, the model supports the development of tools that can learn about the user's context, based on data obtained via sensors installed on users or in their home, as well as data supplied directly by the user interface of their mobile devices, and data provided by the healthcare team, and, after that, recommend contents about their diseases.

User profileMultimediaComputer sciencebusiness.industryContext (language use)Ontology (information science)computer.software_genreWorld Wide WebHealth careUser interfacebusinessMobile devicecomputerUbiquitous learningProceedings of the 7th Euro American Conference on Telematics and Information Systems
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A Virtual Shopper Customer Assistant in Pervasive Environments

2007

In this work we propose a smart, human-like PDA-based personal shopper assistant. The system is able to understand the user needs through a spoken natural language interaction and then stores the preferences of the potential customer. Subsequently the personal shopper suggests the most suitable items and shops that match the user profile. The interaction is given by automatic speech recognition and text-to-speech technologies; localization is allowed by the use of Wireless technologies, while the interaction is performed by an Alice-based chat-bot endowed with reasoning capabilities. Besides, being implemented on a PDA, the personal shopper satisfies the user needs of mobility and it is als…

World Wide WebUser profileNatural language interactionComputer sciencebusiness.industryPervasive systemsWirelessUser needsUSablebusinessMobile device
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The Adaptation of a Web Information System: A Perspective of Organizations

2011

We provide a different view on the problem of Web Information System (WIS) adaptation, looking from perspective of organizations that are interested in an adapted Web Information System for their needs if a unified system to support similar business processes is used. We propose an adaptation architecture for WIS. Two levels of adaptation are introduced—coarse grained adaptation for the organization level and fine grained adaptation for the user level. The architecture supports also the situation, when users can work with many instances of the system adapted for different organizations, which are integrated into one instance for a particular user.

World Wide WebWeb standardsUser profileKnowledge managementbusiness.industryBusiness processComputer scienceArchitecturebusinessAdaptation (computer science)Web intelligenceSoftware product lineWeb information system
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User profile matching in social networks

2010

International audience; Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) w…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Matching (statistics)Computer science[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarity measurecomputer.software_genreElectronic mail[SCCO.COMP] Cognitive science/Computer science020204 information systemsFOAF0202 electrical engineering electronic engineering information engineeringPattern matchingUser profileSocial networkbusiness.industrycomputer.file_formatProfile MatchingSocial Networks[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businesscomputerData integration
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Semantic User Profiling for Digital Advertising

2015

International audience; With the emergence of real-time distribution of online advertising space (“real-time bidding”), user profiling from web navigation traces becomes crucial. Indeed, it allows online advertisers to target customers without interfering with their activities. Current techniques apply traditional methods as statistics and machine learning, but suffer from their limitations. As an answer, the proposed approach aims to develop and evaluate a semantic-based user profiling system for digital advertising.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Data AnalysisBig DataACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[ INFO ] Computer Science [cs]OntologyACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingACM : H.: Information SystemsUser ProfilingACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONSReasoningACM : H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS[INFO] Computer Science [cs][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesACM: H.: Information SystemsInferenceACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[INFO]Computer Science [cs]Logical Rules[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingSWRLACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesSemantic Web
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Customizing Semantic Profiling for Digital Advertising

2014

International audience; Personalization is the new magic buzzword of application development. To make the complexity of today's application functionalities and information spaces "digestible", customization has become the new go-to technique. But while those technologies aim to ease the consumption of media for their users, they suffer from the same problematic: in the age of Big Data, applications have to cope with a conundrum of heterogeneous information sources that have to be perceived, processed and interpreted. Researchers tend to aim for a maximum degree of integration to create the perfect, all-embracing personalization. The results are wide-range, but overly complex systems that su…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][ INFO ] Computer Science [cs]Computer scienceBig dataComplex systemsemantic technologies02 engineering and technology[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]Personalization[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineeringProfiling (information science)Heterogeneous information[ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL][INFO]Computer Science [cs]user profiles[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OWLuser profilingbusiness.industryScalabilitySemantic technology020201 artificial intelligence & image processingbusinessDigital advertisingcustomization
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